{"id":"W4404459465","doi":"10.1007/s00521-024-10556-w","title":"Representation ensemble learning applied to facial expression recognition","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computational Science and Engineering; Computer science; Facial expression recognition; Representation (politics); Artificial intelligence; Ensemble learning; Expression (computer science); Pattern recognition (psychology); Facial expression; Machine learning; Speech recognition; Facial recognition system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001250054,0.0001053439,0.00009615225,0.0001290684,0.0003879872,0.0003660634,0.0001548299,0.00005086679,0.000007697897],"category_scores_gemma":[0.00001669404,0.00009965255,0.00003356292,0.0005061796,0.00001450151,0.0002132606,0.0001613805,0.0001878301,0.0002129807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001327073,"about_ca_system_score_gemma":0.00001188328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007436167,"about_ca_topic_score_gemma":8.542307e-7,"domain_scores_codex":[0.9989749,0.00004324984,0.0001782208,0.0004822125,0.0001499529,0.0001714363],"domain_scores_gemma":[0.9995078,0.0001479692,0.00003710634,0.0001651744,0.00004359598,0.00009837579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003134163,0.0000122547,0.00002007238,0.00001988936,0.000002408136,0.000001106816,0.0005352247,0.0009883523,0.1170642,0.001381449,0.001038939,0.878933],"study_design_scores_gemma":[0.00058282,0.0001837959,0.002110585,0.0004382909,0.00003271969,0.00008208805,0.0007298283,0.6660146,0.2120383,0.02412001,0.09271263,0.0009543361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1541357,0.00008702651,0.8408203,0.0005600221,0.0001515105,0.0003684077,0.000002397465,0.0007105576,0.003164096],"genre_scores_gemma":[0.9828308,0.00001298391,0.01640485,0.0002358319,0.0002252921,0.0001099186,0.00003877454,0.000009884671,0.0001316175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8779787,"threshold_uncertainty_score":0.4063713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135945182487265,"score_gpt":0.3013460778522001,"score_spread":0.2699866260273275,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}